EVALUATION OF NON-STATIONARITY IN ANNUAL MAXIMUM FLOOD SERIES OF MODERATELY IMPAIRED WATERSHEDS IN THE UPPER MIDWEST AND NORTHEASTERN UNITED STATES

United States federal agencies assess flood risk using Bulletin 17B procedures which assume annual maximum flood series are stationary. This represents a significant limitation of current flood frequency models as the flood distribution is thereby assumed to be unaffected by trends or periodicity of atmospheric/climatic variables and/or anthropogenic activities. The validity of this assumption is at the core of this thesis, which aims to improve understanding of the forms and potential causes of non-stationarity in flood series for moderately impaired watersheds in the Upper Midwest and Northeastern US. Prior studies investigated non-stationarity in flood series for unimpaired watersheds; however, as the majority of streams are located in areas of increasing human activity, relative and coupled impacts of natural and anthropogenic factors need to be considered such that non-stationary flood frequency models can be developed for flood risk forecasting over relevant planning horizons for large scale water resources planning and management.

Computational Evaluation of Wind Loads on Low- and High- Rise Buildings

Buildings and other infrastructures located in the coastal regions of the US have a higher level of wind vulnerability. Reducing the increasing property losses and causalities associated with severe windstorms has been the central research focus of the wind engineering community. The present wind engineering toolbox consists of building codes and standards, laboratory experiments, and field measurements. The American Society of Civil Engineers (ASCE) 7 standard provides wind loads only for buildings with common shapes. For complex cases it refers to physical modeling. Although this option can be economically viable for large projects, it is not cost-effective for low-rise residential houses. To circumvent these limitations, a numerical approach based on the techniques of Computational Fluid Dynamics (CFD) has been developed. The recent advance in computing technology and significant developments in turbulence modeling is making numerical evaluation of wind effects a more affordable approach. The present study targeted those cases that are not addressed by the standards. These include wind loads on complex roofs for low-rise buildings, aerodynamics of tall buildings, and effects of complex surrounding buildings. Among all the turbulence models investigated, the large eddy simulation (LES) model performed the best in predicting wind loads. The application of a spatially evolving time-dependent wind velocity field with the relevant turbulence structures at the inlet boundaries was found to be essential. All the results were compared and validated with experimental data. The study also revealed CFD’s unique flow visualization and aerodynamic data generation capabilities along with a better understanding of the complex three-dimensional aerodynamics of wind-structure interactions. With the proper modeling that realistically represents the actual turbulent atmospheric boundary layer flow, CFD can offer an economical alternative to the existing wind engineering tools. CFD’s easy accessibility is expected to transform the practice of structural design for wind, resulting in more wind-resilient and sustainable systems by encouraging optimal aerodynamic and sustainable structural/building design. Thus, this method will help ensure public safety and reduce economic losses due to wind perils.

Physics-Based Modeling of Power System Components for the Evaluation of Low-Frequency Radiated Electromagnetic Fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system’s EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter’s components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled

Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage.

Improved inference in the evaluation of mutual fund performance using bootstrap methods

Two new methodologies are introduced to improve inference in the evaluation of mutual fund performance against benchmarks. First, the benchmark models are estimated using panel methods with both fund and time effects. Second, the non-normality of individual mutual fund returns is accounted for by using panel bootstrap methods. We also augment the standard benchmark factors with fund-specific characteristics, such as fund size. Using a dataset of UK equity mutual fund returns, we find that fund size has a negative effect on the average fund manager’s benchmark-adjusted performance. Further, when we allow for time effects and the non-normality of fund returns, we find that there is no evidence that even the best performing fund managers can significantly out-perform the augmented benchmarks after fund management charges are taken into account.

Evaluation of dry protonated calcium alginate beads for biosorption applications and studies of lead uptake

Alginate polysaccharide is a promising biosorbent for metal uptake. Dry protonated calcium alginate beads for biosorption applications were prepared, briefly characterized and tested for lead uptake. Several advantages of this biosorbent are reported and discussed in comparison with other alginate-based sorbents. The alginate beads contained 4.7 mmol/g of COOH groups, which suffered hydrolysis near pH 4. The Weber and Morris model, applied to kinetic results of lead uptake, showed that intraparticle diffusion was the rate-controlling step in lead sorption by dry alginate beads. Equilibrium experiments were performed and the data were fitted with different isotherm models. The Langmuir equation was the most adequate to model lead sorption. The maximum uptake capacity (qmax) was estimated as 339 mg/g and the Langmuir constant (b) as 0.84 l/mg. These values were compared with that of other sorbents found in the literature, indicating that dry protonated calcium alginate beads are among the best biosorbents for the treatment and recovery of heavy metals from aqueous streams.

Evaluation of dry protonated calcium alginate beads for biosorption applications and studies of lead uptake

Alginate polysaccharide is a promising biosorbent for metal uptake. Dry protonated calcium alginate beads for biosorption applications were prepared, briefly characterized and tested for lead uptake. Several advantages of this biosorbent are reported and discussed in comparison with other alginate-based sorbents. The alginate beads contained 4.7 mmol/g of COOH groups, which suffered hydrolysis near pH 4. The Weber and Morris model, applied to kinetic results of lead uptake, showed that intraparticle diffusion was the rate-controlling step in lead sorption by dry alginate beads. Equilibrium experiments were performed and the data were fitted with different isotherm models. The Langmuir equation was the most adequate to model lead sorption. The maximum uptake capacity (qmax) was estimated as 339 mg/g and the Langmuir constant (b) as 0.84 l/mg. These values were compared with that of other sorbents found in the literature, indicating that dry protonated calcium alginate beads are among the best biosorbents for the treatment and recovery of heavy metals from aqueous streams.

Determinants of Terminal Value in the Evaluation of Companies: A Panel Data Approach to the Context of European Companies.

The uncertainty of the future of a firm has to be modelled and incorporated into the evaluation of companies outside their explicit period of analysis, i.e., in the continuing or terminal value considered within valuation models. However, there is a multiplicity of factors that influence the continuing value of businesses which are not currently being considered within valuation models. In fact, ignoring these factors may cause significant errors of judgment, which can lead models to values of goodwill or badwill, far from the substantial value of the inherent assets. Consequently, these results provided will be markedly different from market values. So, why not consider alternative models incorporating life expectancy of companies, as well as the influence of other attributes of the company in order to get a smoother adjustment between market price and valuation methods? This study aims to provide a contribution towards this area, having as its main objective the analysis of potential determinants of firm value in the long term. Using a sample of 714 listed companies, belonging to 15 European countries, and a panel data for the period between 1992 and 2011, our results show that continuing value cannot be regarded as the current value of a constant or growth perpetuity of a particular attribute of the company, but instead be according to a set of attributes such as free cash flow, net income, the average life expectancy of the company, investment in R&D, capabilities and quality of management, liquidity and financing structure.

Preference inference based on Pareto models

In this paper, we consider Preference Inference based on a generalised form of Pareto order. Preference Inference aims at reasoning over an incomplete specification of user preferences. We focus on two problems. The Preference Deduction Problem (PDP) asks if another preference statement can be deduced (with certainty) from a set of given preference statements. The Preference Consistency Problem (PCP) asks if a set of given preference statements is consistent, i.e., the statements are not contradicting each other. Here, preference statements are direct comparisons between alternatives (strict and non-strict). It is assumed that a set of evaluation functions is known by which all alternatives can be rated. We consider Pareto models which induce order relations on the set of alternatives in a Pareto manner, i.e., one alternative is preferred to another only if it is preferred on every component of the model. We describe characterisations for deduction and consistency based on an analysis of the set of evaluation functions, and present algorithmic solutions and complexity results for PDP and PCP, based on Pareto models in general and for a special case. Furthermore, a comparison shows that the inference based on Pareto models is less cautious than some other types of well-known preference model.

Evaluation of the impact of nursing clinics in the rheumatology services

Nursing clinics in rheumatology (NCRs) are organisational care models that provide care centred within the scope of a nurse’s abilities. To analyse the impact of NCR in the rheumatology services, national multicenter observational prospective cohort studied 1-year follow-up, comparing patients attending rheumatology services with and without NCR. NCR was defined by the presence of: (1) office itself; (2) at least one dedicated nurse; and (3) its own appointment schedule. Variables included were (baseline, 6 and 12 months): (a) test to evaluate clinical activity of the disease, research and training, infrastructure of unit and resources of NCR and (b) tests to evaluate socio-demographics, work productivity (WPAI), use of services and treatments and quality of life. A total of 393 rheumatoid arthritis and ankylosing spondylitis patients were included: 181 NCR and 212 not NCR, corresponding to 39 units, 21 with NCR and 18 without NCR (age 53 + 11.8 vs 56 + 13.5 years). Statistically significant differences were found in patients attended in sites without NCR, at some of the visits (baseline, 6 or 12 months), for the following parameters: higher CRP level (5.9 mg/l ± 8.3 vs 4.8 mg/l ± 7.8; p < 0.005), global disease evaluation by the patient (3.6 ± 2.3 vs 3.1 ± 2.4), physician (2.9 ± 2.1 vs 2.3 ± 2.1; p < 0.05), use of primary care consultations (2.7 ± 5.4 vs 1.4 ± 2.3; p < 0.001) and worse work productivity. The presence of NCR in the rheumatology services contributes to improve some clinical outcomes, a lower frequency of primary care consultations and better work productivity of patients with rheumatic diseases.

Lighting ambiances and materialities of wood in architecture: a comparative evaluation of the quality of spaces in relation to interior finishes

Le bois est un matériau souvent utilisé par les architectes pour améliorer l’ambiance générale d’un espace, mais peu de recherches en présentent l’impact réel du matériau sur les impressions visuelles et les effets lumineux. Cette recherche étudie l’influence de la matérialité du bois par rapport à la création d’ambiances d’éclairage spécifiques dans l’architecture. Plus particulièrement, elle se concentre sur l’impact des panneaux décoratifs en bois à générer de la diversité lumineuse dans les espaces intérieurs et son potentiel à améliorer la satisfaction environnementale et l’efficacité énergétique. La recherche utilise des maquettes à l’échelle pour leur précision dans la représentation des ambiances lumineuses d’espaces éclairés naturellement ainsi que les technologies récentes d’imagerie digitale pour capturer et analyser les résultats. La méthodologie permet la comparaison entre les différents réglages des espaces intérieurs créés par une sélection des types de matérialités du bois: la réflectance (valeur), la couleur et la réflectivité. Les modalités spatiales sont comparées en présence d’ensoleillement direct et sous des conditions de ciel couvert puisque les modèles d’éclairage et les ambiances diffèrent considérablement. Les résultats permettent d’établir une discussion sur les ambiances en termes de brillance et de contraste, sur la couleur ainsi que la répartition des zones lumineuses dans l’espace. La recherche souligne le rôle des matérialités que peuvent prendre le bois pour optimiser la diversité lumineuse et la création d’ambiances visuellement confortables, ainsi que ses possibilités d’améliorer les ambiances architecturales par rapport à la lumière.

Discrimination capacity in species distribution models depends on the representativeness of the environmental domain

Aim When faced with dichotomous events, such as the presence or absence of a species, discrimination capacity (the ability to separate the instances of presence from the instances of absence) is usually the only characteristic that is assessed in the evaluation of the performance of predictive models. Although neglected, calibration or reliability (how well the estimated probability of presence represents the observed proportion of presences) is another aspect of the performance of predictive models that provides important information. In this study, we explore how changes in the distribution of the probability of presence make discrimination capacity a context-dependent characteristic of models. For the first time,we explain the implications that ignoring the context dependence of discrimination can have in the interpretation of species distribution models.

New measures for assessing model equilibrium and prediction mismatch in species distribution models

Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species’ distributions under climate and land-use change

How well can models predict changes in species distributions? A 13-year-old otter model revisited

Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of environmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas,m but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.

Effect of inclusion or non-inclusion of short lactations and cow and/or dam genetic group on genetic evaluation of Girolando dairy cattle.

The objective of this study was to evaluate the effects of inclusion or non-inclusion of short lactations and cow (CGG) and/or dam (DGG) genetic group on the genetic evaluation of 305-day milk yield (MY305), age at first calving (AFC), and first calving interval (FCI) of Girolando cows. Covariance components were estimated by the restricted maximum likelihood method in an animal model of single trait analyses. The heritability estimates for MY305, AFC, and FCI ranged from 0.23 to 0.29, 0.40 to 0.44, and 0.13 to 0.14, respectively, when short lactations were not included, and from 0.23 to 0.28, 0.39 to 0.43, and 0.13 to 0.14, respectively, when short lactations were included. The inclusion of short lactations caused little variation in the variance components and heritability estimates of traits, but their non-inclusion resulted in the re-ranking of animals. Models with CGG or DGG fixed effects had higher heritability estimates for all traits compared with models that consider these two effects simultaneously. We recommend using the model with fixed effects of CGG and inclusion of short lactations for the genetic evaluation of Girolando cattle.